PATH Framework Refines Reinforcement Learning Curricula

In many reinforcement learning (RL) scenarios, environments are naturally structured by prerequisite relationships, forming a directed acyclic graph (DAG) where difficulty increases or parameters increment. While this structure is often implicitly used, explicitly modeling it can significantly enhance the training process. A novel curriculum-learning framework called PATH has been introduced to capitalize on this inherent structure. PATH operates by performing active learning directly over the curriculum graph. It first broadens the training scope by sampling a diverse range of curriculum paths, ensuring comprehensive coverage of the learning space. Following this, it intelligently reallocates training efforts, focusing more intensely on areas or environments that the agent has not yet mastered. Experiments conducted across various diverse environments demonstrate that PATH effectively utilizes the explicit graph structure. This strategic leveraging of the curriculum graph leads to substantial improvements in the robustness and generalization capabilities of the trained RL agents, making them more adaptable to new or varied conditions.